This is the question that’s been bugging me. Latenode gives you access to hundreds of AI models in one subscription. OpenAI, Claude, Deepseek, and tons others. That’s amazing for flexibility, but it also creates this weird decision problem.
When you’re building a RAG workflow, you need to pick a model for retrieval and a model for generation. Do they have to be different? Should retrieval use a smaller, faster model and generation use something more powerful? Or does it matter at all?
I’ve seen people just pick the same model for both steps. I’ve also seen people obsess over finding the “perfect” combination. I’m trying to understand what actually matters here versus what’s just overthinking it.
Has anyone done real A/B testing on this? Like, did switching from one model to another actually make a measurable difference in your RAG output quality, or is the effect minimal in practice?
Retrieval is really just about relevance. You want a model that’s good at understanding what documents match your query. Smaller models work fine here. They’re faster and cheaper.
Generation is where model quality matters more. This is where you turn retrieved documents into actual answers. A better model here produces clearer, more accurate responses.
So practical strategy: use a fast, efficient model for retrieval. Use your best available model for generation. That gives you accuracy where it counts without burning budget on retrieval overhead.
With Latenode’s 400 models in one subscription, you can experiment for free basically. Pick a combo, test it against your real queries, see if it works. If not, swap a model and try again. The cost difference is tiny when you’re not juggling multiple API keys and subscriptions.
What I found is that retrieval model doesn’t matter almost at all. The difference between Claude and GPT-4 for just finding relevant documents is negligible. Where model choice actually affects output is generation. The quality of the final answer varies noticeably based on which generation model you use. So my advice: use whatever retrieval model is cheapest and fastest. Spend your quality focus on the generation model.
I spent way too much time optimizing this. Turns out the retrieval model matters less than having clean, well-structured documents to retrieve from. Garbage in, garbage out applies even with perfect retrieval. The generation model matters more because that’s where user-facing quality happens. So focus there first.